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Data Analytics (Eng) / Data Analitika (Ing) - 344

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When inducing a tree, one should use the testing performance instead of the validation performance to decide which measure should be used to determine split criteria.

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Which of the following statements are false about decision trees? Select one or more. Incorrect answers will be penalized.

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If all the instances in a dataset (D) are of the same class, then the entropy is
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If 3 quarters, 1 quarter and none of the instances in a dataset (D) are of target feature classes A, B and C, respectively, then the gini index of D is

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A classification tree that is applied to a problem with two classes can achieve a maximum entropy information gain of 0.5 at any node in the tree.
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Which of the following statements are true about decision tree induction? Select one or more. Incorrect answers will be penalized.

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When comparing the rules extracted from a non-oblique tree against the rules extracted from an oblique tree, the non-oblique tree in general produces less rules when both trees are trained on the same training set.
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If half, half and none of the instances in a dataset (D) are of target feature classes A, B and C, respectively, then the entropy of D is

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When comparing the total number of tree levels in a model tree against the total number of tree levels in an ordinary regression tree trained on the same dataset and using the same pre-pruning strategy, the model tree in general produces fewer tree levels.
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The target values of an unbalanced dataset of 10 instances (I1 up to I10) are 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, respectively. An evaluation of a decision tree on this dataset determines that the tree's predictions of the instances I1, I5, I6 and I7 are true negative, false negative, false positive and false negative, respectively. If the accuracy of the decision tree on the dataset is 0.7, what is its precision?

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